1/ Why did Wikipedia succeed when 7 similar online encyclopedia projects (mostly started around the same time) all failed? This cool paper investigates and gives surprising answers...

2/ Did Wiki have the most technical talent? No, they had the *least* technical founders by far. One failed project was led by Aaron Swartz (RSS + Reddit creator) and one by the founder of Slashdot. Wiki's initial software was off-the-shelf.
3/ Wiki's 1st source of success: a familiar end-product. Use a novel mechanism (online collaboration) to produce a trad encyclopedia. Some failed projects aimed for new kind of encyclopedia for internet age and this confused contributors.
4/ The E2 project was meant to be broader than Wiki. However, it was hard to define to new users just how broad (see hilarious quote from its FAQ). A project called "h2g2" kept getting h2g2-style submissions, despite not wanting them.
5/ Many of biggest open-source or crowdsourced projects have familiar end-products: Linux, Apache, OpenOffice, StackOverflow, gcc, scientific Python. If building a community, beware novel goals!
6/ Wiki's 2nd+3rd sources of success: low barrier to contribute and no public credited author. Some failed projects had credited authors which put off other contributors from editing (to avoid stepping on toes).
7/ The table shows Wiki was 1/8 projects with these three features. The paper author interviewed all founders (and examined archives) to determine the features of each project.
8/ This paper is not the final word. But I'd love more papers like this. Why Craigslist and not all the other projects? Why Gmail? Why StackOverflow?
9/ P.S. I believe the paper author was himself a serious contributor to Wikipedia and to FLOSS projects (Linux) and so has more insider knowledge than others might.

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More from @OwainEvans_UK

21 Nov
FaceApp is trained to modify photos of faces (e.g. Instagram). How well does it generalize to paintings? Surprisingly well.

We can send Marilyn into the painting world (German expressionism from 1930), and pull the painting's subject into reality. ImageImageImage
Here's a portrait by Rita Angus FaceApped to star Cate Blanchette. FaceApp preserves some (but not all) of distinctive stylized rendering of the face. ImageImage
Another stylized portrait by Rita Angus. Image
Read 7 tweets
20 Nov
1/ Second thread on exciting philosophy from outside philosophy departments...
2/ Gerry Sussman. Hofstadter said Gödel invented LISP in proving the incompleteness theorem. Sussman shows the amazing breadth and elegance of LISP ideas. SICP, SICM, How to build robust systems. google.com/url?sa=t&rct=j… Image
3/ Eric Drexler. Engineering is neglected by philosophy departments (see Sussman also). Engines, Nanosystems, how engineering differs from science, CAIS. Disclosure: he's currently at the FHI (which is part of a phil dept).
Read 8 tweets
11 Nov
1/ Thread for exciting philosophy being done outside university philosophy departments. Descartes, Hobbes, Hume, Mill, Frege, Ramsey, and Turing all worked outside phil academia. Here are some contemporary examples...
2/ David Deutsch: foundations of Quantum computation, Many Worlds, Fabric of Reality, Beginning of Infinity, Constructor Theory
3/ Paul Christiano: AI Safety (IDA, Debate), assigning probabilities to FOL and foundations of game theory, certificates of impact.
Read 8 tweets
5 Jun
1. What % of people have natural immunity to Covid? We get some information from closed environments where a large % of people were exposed. Here are some numbers from prisons, a meat plant, and call center. From this, seems that >80% are susceptible under the right conditions.
2. Note that only the Korean call-center number is based on a scientific study. However, the call center was shut down early and so it's likely that >55% would have been infected if it had stayed open. I think >65% is plausible for prisons, but not sure about Marion result.
3. Some hospital wards and care homes also had large %es infected. But older people are more susceptible. Prisons, meat plants and call centers cover a wide range of ages (18-70). In the table I adjust for the false-negative rate of PCR testing, which is ~20-30% for mass testing.
Read 8 tweets

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